fix gen alg

astar.py

@@ -3,124 +3,108 @@ from board import Board
 from constant import width, height, rows, cols
 from tractor import Tractor
 import heapq
-import math
 
 fps = 2
 WIN = pygame.display.set_mode((width, height))
 pygame.display.set_caption('Inteligenty Traktor')
 
+
 class Node:
-    def __init__(self, x, y):
+    def __init__(self, state, parent=None, action=None, cost=0):
-        self.x = x
+        self.state = state  # Stan reprezentowany przez węzeł
-        self.y = y
+        self.parent = parent  # Węzeł rodzica
-        self.f = 0
+        self.action = action  # Akcja prowadząca do tego stanu
-        self.g = 0
+        self.cost = cost  # Koszt przejścia do tego stanu
-        self.h = 0
+        self.f = 0  # Wartość funkcji priorytetowej
-        self.cost = 1
+        self.tie_breaker = 0  # Wartość używana do rozwiązywania konfliktów priorytetów
-        self.visited = False
-        self.closed = False
-        self.parent = None
 
     def __lt__(self, other):
+        # Porównanie węzłów w celu ustalenia kolejności w kolejce priorytetowej
+        if self.f == other.f:
+            return self.tie_breaker > other.tie_breaker  # Większy tie_breaker ma wyższy priorytet
         return self.f < other.f
-
+        # Jesli pare wezlow ma taie same f, to tilebreaker ustawia
-    def neighbors(self, grid):
+        # prorytety akcje right i down maja wyzszy priorytet
-        ret = []
-        x, y = self.x, self.y
-        if x > 0 and grid[x - 1][y]:
-            ret.append(grid[x - 1][y])
-        if x < len(grid) - 1 and grid[x + 1][y]:
-            ret.append(grid[x + 1][y])
-        if y > 0 and grid[x][y - 1]:
-            ret.append(grid[x][y - 1])
-        if y < len(grid[0]) - 1 and grid[x][y + 1]:
-            ret.append(grid[x][y + 1])
-        return ret
-
-def init(grid):
-    for x in range(len(grid)):
-        for y in range(len(grid[x])):
-            node = grid[x][y]
-            node.f = 0
-            node.g = 0
-            node.h = 0
-            node.cost = 1
-            node.visited = False
-            node.closed = False
-            node.parent = None
-
-def heap():
-    return []
-
-def search(grid, start, end, board, heuristic=None):
-    init(grid)
-    if heuristic is None:
-        heuristic = manhattan
-    open_heap = heap()
-    heapq.heappush(open_heap, start)
-
-    while open_heap:
-        current_node = heapq.heappop(open_heap)
-        if (current_node.x, current_node.y) == (end.x, end.y):
-
-            ret = []
-            while current_node.parent:
-                ret.append(current_node)
-                current_node = current_node.parent
-            ret.append(start)    
-            ret_path = ret[::-1]
-            for node in ret_path:
-                print(f"({node.x}, {node.y}): {node.g}")
-            print("Znaleziono ścieżkę [(x,y)jako(kolumna,wiersz)] o koszcie:", ret_path[-1].g)
-            return ret_path, start 
-        current_node.closed = True
-        for neighbor in current_node.neighbors(grid):
-            if neighbor.closed:  
-                continue
-            g_score = current_node.g + board.get_cost(neighbor.x, neighbor.y)  
-            been_visited = neighbor.visited
-            if not been_visited or g_score < neighbor.g:
-                neighbor.visited = True
-                neighbor.parent = current_node
-                neighbor.h = neighbor.h or heuristic((neighbor.x, neighbor.y), (end.x, end.y))
-                neighbor.g = g_score
-                neighbor.f = neighbor.g + neighbor.h
-                if not been_visited:
-                    heapq.heappush(open_heap, neighbor)
-    
-    print("Nie znaleziono ścieżki.")
-    return None
 
 
 def manhattan(pos0, pos1):
+    # Heurystyka odległości Manhattan
     d1 = abs(pos1[0] - pos0[0])
     d2 = abs(pos1[1] - pos0[1])
     return d1 + d2
 
 
+def nastepnik(state, board):
+    # Funkcja generująca możliwe następne stany (akcje)
+    x, y = state
+    successors = []
+    actions = [('right', (x+1, y), 1), ('down', (x, y+1), 1), ('up', (x, y-1), 0), ('left', (x-1, y), 0)]
+    for action, next_state, tie_breaker in actions:
+        if 0 <= next_state[0] < cols and 0 <= next_state[1] < rows:
+            cost = board.get_cost(next_state[0], next_state[1])
+            successors.append((action, next_state, cost, tie_breaker))
+    return successors
+
+
+def goal_test(state, goal):
+    #  Czy dany stan jest stanem docelowym
+    return state == goal
+
+
+def graphsearch(istate, goal, board, heuristic=manhattan):
+    # Algorytm przeszukiwania grafu
+    fringe = []  # Kolejka priorytetowa przechowująca węzły do odwiedzenia
+    explored = set()  # Zbiór odwiedzonych stanów
+    start_node = Node(istate)
+    start_node.f = heuristic(istate, goal)  # Obliczenie wartości heurystycznej dla stanu początkowego
+    start_node.tie_breaker = 0  # Ustawienie tie_breaker dla węzła startowego,
+    heapq.heappush(fringe, start_node)
+
+    while fringe:
+        elem = heapq.heappop(fringe)
+        if goal_test(elem.state, goal):
+            path = []
+            total_cost = elem.cost  # Zapisanie całkowitego kosztu
+            while elem:
+                path.append((elem.state, elem.action))
+                elem = elem.parent
+            return path[::-1], total_cost  # Zwrócenie ścieżki i kosztu
+
+        explored.add(elem.state)
+        for action_index, (action, state, cost, tie_breaker) in enumerate(nastepnik(elem.state, board)):
+            x = Node(state, parent=elem, action=action, cost=elem.cost + cost)
+            x.f = x.cost + heuristic(state, goal)  # Obliczenie wartości funkcji priorytetowej
+            x.tie_breaker = elem.tie_breaker * 4 + action_index  # Obliczanie tie_breaker na podstawie akcji
+
+            if state not in explored and not any(node.state == state for node in fringe):
+                heapq.heappush(fringe, x)
+            else:
+                for i, node in enumerate(fringe):
+                    if node.state == state and (node.f > x.f or (node.f == x.f and node.tie_breaker < x.tie_breaker)):
+                        fringe[i] = x
+                        heapq.heapify(fringe)
+                        break
+
+    print("Nie znaleziono ścieżki.")
+    return None, 0  # Zwrócenie ścieżki jako None i kosztu jako 0 w przypadku braku ścieżki
+
 def main():
     run = True
     clock = pygame.time.Clock()
     board = Board()
     board.load_images()
-    
+
-    start_row, start_col = 0,0
+    start_state = (9, 9)  # Stan początkowy
-    end_row, end_col = 9,9
+    goal_state = (0, 0)  # Stan docelowy
-    tractor = Tractor(start_row, start_col)  
+    tractor = Tractor(start_state[1], start_state[0])
-    board.set_grass(start_row, start_col)
+    board.set_grass(start_state[0], start_state[1])  # Ustawienie startowego pola jako trawę
-    board.set_grass(end_row, end_col)
+    board.set_grass(goal_state[0], goal_state[1])  # Ustawienie docelowego pola jako trawę
-   
+
-    grid = [[Node(x, y) for y in range(rows)] for x in range(cols)]
+    path, total_cost = graphsearch(start_state, goal_state, board)
-    
-    start = grid[start_row][start_col]
-    end = grid[end_row][end_col]
-    
-    path, start_node = search(grid, start, end, board)
-    
 
     while run:
         clock.tick(fps)
-   
+
         for event in pygame.event.get():
             if event.type == pygame.QUIT:
                 run = False
@@ -129,39 +113,38 @@ def main():
             run = False
             continue
 
-        next_node = path.pop(0) if path else start_node
+        next_state, action = path.pop(0) if path else (start_state, None)
-        dx = next_node.x - tractor.col
+        print(next_state)  # Wypisanie następnego stanu
-        dy = next_node.y - tractor.row
+        tractor.row, tractor.col = next_state[1], next_state[0]
-        tractor.row, tractor.col = next_node.y, next_node.x
 
-       
+        if action == "right":
-        if   dx > 0:
             tractor.direction = "right"
-        elif dx < 0:
+        elif action == "left":
             tractor.direction = "left"
-        elif dy > 0:
+        elif action == "down":
             tractor.direction = "down"
-        elif dy < 0:
+        elif action == "up":
             tractor.direction = "up"
-            
+
-        if board.is_weed(tractor.col, tractor.row ):
+        # Aktualizacja planszy na podstawie położenia traktora
-                    board.set_grass(tractor.col, tractor.row )
+        if board.is_weed(tractor.col, tractor.row):
-                  
+            board.set_grass(tractor.col, tractor.row)
-        elif board.is_dirt(tractor.col, tractor.row ):
+        elif board.is_dirt(tractor.col, tractor.row):
-                    board.set_soil(tractor.col, tractor.row )
+            board.set_soil(tractor.col, tractor.row)
-                    
+        elif board.is_soil(tractor.col, tractor.row):
-        elif board.is_soil(tractor.col, tractor.row ):
+            board.set_carrot(tractor.col, tractor.row)
-                    board.set_carrot(tractor.col, tractor.row )
-                     
 
         board.draw_cubes(WIN)
         tractor.draw(WIN)
         pygame.display.update()
 
+    print(f"Całkowity koszt trasy: {total_cost}")
+
     while True:
         for event in pygame.event.get():
             if event.type == pygame.QUIT:
                 pygame.quit()
                 return
 
+
 main()

board.py

@@ -10,6 +10,7 @@ class Board:
         self.load_costs()
 
 
+
     def load_images(self):
         self.grass = pygame.image.load("board/grass.png")
         self.dirt = pygame.image.load("board/dirt.png")
@@ -19,37 +20,52 @@ class Board:
         self.carrot = pygame.image.load("board/carrot.png")
 
     def generate_board(self):
-        self.board = [[random.choice([0,1,2,3,4,5,6,7,8,9]) for _ in range(rows)] for _ in range(cols)]
+        # Najpierw wypełniamy całą planszę trawą (kod 2)
+        self.board = [[2 for _ in range(rows)] for _ in range(cols)]
+
+        # Losowo wybieramy 5 unikalnych pozycji dla chwastów
+        weed_positions = random.sample([(row, col) for row in range(rows) for col in range(cols)], 5)
+
+        # Umieszczamy chwasty na wylosowanych pozycjach
+        for row, col in weed_positions:
+            self.board[row][col] = 1  # 1 oznacza chwast
+
+        # Teraz losowo umieszczamy inne elementy, omijając pozycje chwastów
+        for row in range(rows):
+            for col in range(cols):
+                if (row, col) not in weed_positions:
+                    # Losujemy typ terenu, ale pomijamy kod 1 (chwast)
+                    self.board[row][col] = random.choice([0, 2, 3, 4, 5, 6, 7, 8, 9])
 
     def draw_cubes(self, win):
 
+
         for row in range(rows):
             for col in range(cols):
                 cube_rect = pygame.Rect(row * size, col * size, size, size)
-                cube=self.board[row][col]
+                cube = self.board[row][col]
 
-                if row==4 and col==4:
+                if row == 4 and col == 4:
                     win.blit(self.grass, cube_rect)
                 elif cube == 0:
                     rock_scale = pygame.transform.scale(self.rock, (size, size))
                     win.blit(self.dirt, cube_rect)
-                    win.blit(rock_scale,  cube_rect)
+                    #win.blit(rock_scale, cube_rect)
                 elif cube == 1:
-                    weed_scale = pygame.transform.scale(self.weeds, (size,size))
+                    weed_scale = pygame.transform.scale(self.weeds, (size, size))
                     win.blit(self.grass, cube_rect)
                     win.blit(weed_scale, cube_rect)
-                elif cube in(2,3,4,5):
+                elif cube in (2, 3, 4, 5):
-                    win.blit(self.grass,  cube_rect)
+                    win.blit(self.grass, cube_rect)
                 elif cube == 10:
                     win.blit(self.soil, cube_rect)
                 elif cube == 11:
-                    carrot_scale = pygame.transform.scale(self.carrot, (size,size))
+                    carrot_scale = pygame.transform.scale(self.carrot, (size, size))
                     win.blit(self.carrot, cube_rect)
                     win.blit(carrot_scale, cube_rect)
 
                 else:
-                    win.blit(self.dirt,  cube_rect)
+                    win.blit(self.dirt, cube_rect)
-
 
     def load_costs(self):
             self.costs = {
@@ -78,6 +94,7 @@ class Board:
     def is_weed(self,row,col):
         return self.board[row][col] == 1
 
+
     def set_grass(self,row,col):
         self.board[row][col]=2
 
@@ -93,10 +110,10 @@ class Board:
     def set_carrot(self, row, col):
         self.board[row][col] = 11
 
-    def get_dirt_positions(self):
+    def get_weed_positions(self):
-        dirt_positions = []
+        weed_positions = []
         for row in range(rows):
             for col in range(cols):
-                if self.is_dirt(row, col):
+                if self.is_weed(row, col):
-                    dirt_positions.append([row, col])
+                    weed_positions.append([row, col])
-        return dirt_positions
+        return weed_positions

gen_algorithm.py

@@ -5,36 +5,34 @@ from constant import width, height, size, rows, cols
 from board import Board
 from tractor import Tractor
 
-
 routes_num = 20  # Ilość ścieżek
 board = Board()
-dirt_positions = board.get_dirt_positions()
+weed_positions = board.get_weed_positions()
-dirt_count = len(dirt_positions)
+weed_count = len(weed_positions)
 
 def manhattan(a, b):
     return abs(a[0] - b[0]) + abs(a[1] - b[1])
 
+
 def find_routes(routes_num):
     population_set = []  # zapisujemy trasy - losowe ułóżenia
     for i in range(routes_num):
         # losowo wygenerowane kolejności na trasie
-        single_route = np.random.choice(list(range(dirt_count)), dirt_count, replace=False)
+        single_route = np.random.choice(list(range(weed_count)), weed_count, replace=False)
         population_set.append(single_route)
-    return np.array(population_set)
+    return np.array(population_set) #zwracamy 20 roznych losowych tras
-
 
 def sum_up_for_route(route_indices):
+
     sum = 0
     for i in range(len(route_indices) - 1):
-        current_dirt = dirt_positions[route_indices[i]]
+        current_weed = weed_positions[route_indices[i]]
-        next_dirt = dirt_positions[route_indices[i + 1]]
+        next_weed = weed_positions[route_indices[i + 1]]
-        sum += manhattan(current_dirt, next_dirt)
+        sum += manhattan(current_weed, next_weed)
-    return sum
+    return sum #zwracamy odleglosc (ilosc pol) dla danej trasy manhatanem
 
-
+def routes_sum(population_set):  # zapisujemy na liście finalne sumy odległości dla każdej z tras
-def routes_sum(population_set):  # zapisujemy na liście finalne sumy odległości dla każdej z opcji tras
     list_of_sums = np.zeros(routes_num)
-
     for i in range(routes_num):
         list_of_sums[i] = sum_up_for_route(population_set[i])  # wywołujemy dla każdej trasy na liście
 
@@ -42,29 +40,30 @@ def routes_sum(population_set):  # zapisujemy na liście finalne sumy odległoś
 
 
 def calculate_fitness(distances):
-    # Odwrotność odległości jako fitness
+    # odwrotność odległości jako fitness
-    # Dodajemy małą wartość (np. 1) aby uniknąć dzielenia przez zero
+    # dodajemy małą wartość (np. 1) aby uniknąć dzielenia przez zero
     return 1 / (distances + 1)
 
 
 def selection(population_set, list_of_sums):
-    # Oblicz wartości fitness dla każdej trasy
+    #RULETKA - czesciowo faworyzuje rozwiaznaia, wiekszy fitness wieksze szanse
-    fitness_values = calculate_fitness(list_of_sums)
+    # obliczamy wartości fitness  (przystosowania) dla każdej trasy
-    # Normalizuj wartości fitness, aby sumowały się do 1 (wymagane dla np.random.choice)
+    fitness_values = calculate_fitness(list_of_sums)#krotsze trasy maja miec wyzsze wartosci
+    # normalizujemy wartości fitness, aby sumowały się do 1 (wymagane dla np.random.choice)
     probabilities = fitness_values / fitness_values.sum()
-    # Wybierz rodziców na podstawie prawdopodobieństw (wartości fitness)
+    # wybieramy indeksy rodziców na podstawie prawdopodobieństw
     progenitor_indices_a = np.random.choice(range(len(population_set)), len(population_set), p=probabilities,  replace=True)
     progenitor_indices_b = np.random.choice(range(len(population_set)), len(population_set), p=probabilities, replace=True)
-    # Wybierz rzeczywiste trasy
+    # finalne  trasy
     progenitor_a = population_set[progenitor_indices_a]
     progenitor_b = population_set[progenitor_indices_b]
 
-    return np.array([progenitor_a, progenitor_b])
+    return np.array([progenitor_a, progenitor_b]) #zwracami listy przodkow-rodzicow
 
 def one_point_crossover(parent_a, parent_b): #krzyzowanie jednopunktowe
     crossover_point = np.random.randint(1, len(parent_a))
     child = np.concatenate((parent_a[:crossover_point], [x for x in parent_b if x not in parent_a[:crossover_point]]))
-    return child
+    return child #loosyw punkt przeciecia ktory skleja nam nowa trase, wieksza szans na lepsza tarse
 
 def population_mating(progenitor_list):
     new_population_set = []
@@ -72,24 +71,17 @@ def population_mating(progenitor_list):
         progenitor_a, progenitor_b = progenitor_list[0][i], progenitor_list[1][i]
         child = one_point_crossover(progenitor_a, progenitor_b)
         new_population_set.append(child)
-    return new_population_set
+    return new_population_set # lista potomkow po krzyzowaniu
 
-def mutation_of_child(child):
-    for i in range(dirt_count):  # dla każdego elementu dajemy losową szansę zamiany int *rate
-        x = np.random.randint(0, dirt_count)
-        y = np.random.randint(0, dirt_count)
 
-        child[x], child[y] = child[y], child[x]  # zamiana miejscami
 
-    return child
+def mutation_of_child(child, mutation_rate=0.2):#procent moze pomoc w niezaklucaniu trasy gdy jesy duza trasa ale idk
-
-'''def mutation_of_child(child, mutation_rate=0.1):#procent moze pomoc w niezaklucaniu trasy gdy jesy duza trasa ale idk
     num_mutations = int(len(child) * mutation_rate)
     for _ in range(num_mutations):
-        x = np.random.randint(0, len(child))
+        x = np.random.randint(0, len(child))#losowa szansa zamiany - mutacja
         y = np.random.randint(0, len(child))
         child[x], child[y] = child[y], child[x]
-    return child'''
+    return child#zwrocenie bardziej roznorodnych potomkow
 
 
 def mutate_population(new_population_set):
@@ -100,6 +92,23 @@ def mutate_population(new_population_set):
 
 
 if __name__ == '__main__':
+    pygame.init()
+    WIN = pygame.display.set_mode((width, height))
+    pygame.display.set_caption('Trasa Traktora')
+    clock = pygame.time.Clock()
+
+    board = Board()
+    board.load_images()
+    weed_positions = [(col, row) for col in range(cols) for row in range(rows) if board.is_weed(col, row)]
+    weed_count = len(weed_positions)
+
+    board.set_grass(9, 9)  # pozycja startowa
+    tractor = Tractor(9, 9)  # Start traktora
+
+
+    # Inicjalizacja final_route
+    final_route = [0, float('inf'), np.array([])]
+    # [0]: indeks iteracji, [1]: najlepsza suma odległości, [2]: najlepsza trasa
 
     population_set = find_routes(routes_num)
     list_of_sums = routes_sum(population_set)
@@ -107,6 +116,7 @@ if __name__ == '__main__':
     new_population_set = population_mating(progenitor_list)
     final_mutated_population = mutate_population(new_population_set)
     final_route = [-1, np.inf, np.array([])]  # format listy
+
     for i in range(20):
         list_of_sums = routes_sum(final_mutated_population)
         # zapisujemy najlepsze rozwiązanie
@@ -115,12 +125,64 @@ if __name__ == '__main__':
             final_route[1] = list_of_sums.min()
             final_route[2] = np.array(final_mutated_population)[list_of_sums.min() == list_of_sums]
 
-
         progenitor_list = selection(population_set, list_of_sums)
         new_population_set = population_mating(progenitor_list)
         final_mutated_population = mutate_population(new_population_set)
 
     print(f"Najlepsza trasa znaleziona w iteracji: {final_route[0]}")
     print(f"Minimalna suma odległości: {final_route[1]}")
-    print(f"Kolejne pola: {final_route[2]}")
 
+
+    run = True
+    current_target_index = 0
+    best_routes = final_route[2]  #tablica z najlepszymi trasami
+    visited_fields = []
+    while run:
+        clock.tick(2)  # FPS
+
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                run = False
+
+        for route in best_routes:
+            if current_target_index < len(route):
+                current_weed = weed_positions[route[current_target_index]]
+
+
+                # ruch w kierunku bieżącego celu
+                if tractor.col < current_weed[0]:
+                    tractor.col += 1
+                    tractor.direction = "right"
+                elif tractor.col > current_weed[0]:
+                    tractor.col -= 1
+                    tractor.direction = "left"
+                elif tractor.row < current_weed[1]:
+                    tractor.row += 1
+                    tractor.direction = "down"
+                elif tractor.row > current_weed[1]:
+                    tractor.row -= 1
+                    tractor.direction = "up"
+
+                current_position = (tractor.col, tractor.row)
+                if current_position not in visited_fields:
+                    visited_fields.append(current_position)
+
+                # Jeśli traktor dotarł do celu
+                if (tractor.col, tractor.row) == current_weed:
+                    current_target_index += 1
+
+                # Aktualizacja planszy
+                if board.is_weed(tractor.col, tractor.row):
+                    board.set_carrot(tractor.col, tractor.row)
+
+
+
+        board.draw_cubes(WIN)
+        tractor.draw(WIN)
+        pygame.display.update()
+
+    print("Odwiedzone pola:")
+    for field in visited_fields:
+        print(field)
+
+    pygame.quit()